Bibliographic record
Abstract
The ubiquity of automobiles has made it so that a considerable amount of space is devoted to them in cities. In the past, urban scholars considered these car-dedicated spaces to be ‘non-places’ that hindered place attachment and social life in cities. This article investigates new place-making efforts in these spaces by examining the documentary film Life on Wheels that offers alternatives. Within the ‘new mobility paradigm’, social space is considered an assemblage of social interactions, objects (e.g. technologies), geographical locations, emplacements and communication networks. Through the analysis of the film, this article investigates the social opportunities and challenges that driverless cars may bring to the urban space. More specifically, in this article, the film shows that the technoscape of driverless cars can direct the city towards a shift in socio-spatial urban design and planning. A profound analytical reading illuminates the need for further social development of this technology. While the technoscape of driverless cars in the current state is in its infancy, its produced social space is yet to be scrutinized; However, at this stage of technology development, the film shows that the driverless environment can help us work towards a new way of understanding mobility spaces and their socio-technological characteristics. This article identifies the social urbanism in car-dedicated spaces projected in films and how this social urbanism can be attainable via new technologies or a transformational shift in mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".